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Sketch-to-Art: Synthesizing Stylized Art Images From Sketches

2020-02-26 19:02:10
Bingchen Liu, Kunpeng Song, Ahmed Elgammal
arXiv_CV
         
Abstract

We propose a new approach for synthesizing fully detailed art-stylized images from sketches. Given a sketch, with no semantic tagging, and a reference image of a specific style, the model can synthesize meaningful details with colors and textures. The model consists of three modules designed explicitly for better artistic style capturing and generation. Based on a GAN framework, a dual-masked mechanism is introduced to enforce the content constraints (from the sketch), and a feature-map transformation technique is developed to strengthen the style consistency (to the reference image). Finally, an inverse procedure of instance-normalization is proposed to disentangle the style and content information, therefore yields better synthesis performance. Experiments demonstrate a significant qualitative and quantitative boost over baselines based on previous state-of-the-art techniques, adopted for the proposed process.

Abstract (translated)

URL

https://arxiv.org/abs/2002.12888

PDF

https://arxiv.org/pdf/2002.12888.pdf

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